Improved MPPT of solar PV Systems under different Environmental conditions utilizes a Novel Hybrid PSO

Abstract

Partial shading conditions (PSCs) and their negative impact on photovoltaic system (PVS) performance have been well studied over past decade. This has prompted researchers to explore methods to reduce impact of PV shading. In this study comparison between ant colony optimization (ACO), grey wolf optimization (GWO), cuckoo search (CS), perturb and observe (P&O) algorithm, and particle swarm optimization (PSO) algorithms with hybrid PSO (HPSO) have been investigated under PSC and without PSC. One of ultimate and desired evolutionary research techniques is PSO, which bestow excessive tracking speeds (TS) and has capacity to operate under varying environmental conditions. Many mitigations and enhancements have been made in recent lifespan to address the usual defects traditional PSO techniques. This paper presents a comparative analysis of six parameters as irradiance, PV current, buck boost current, PV voltage, bus voltage, power using different algorithms with respect to hybrid PSO (HPSO). However, the HSPO is a faster convergence rate than the PSO-based MPPT method. At different PSC the 27 % efficiency of PVS is achieved using HPSO. It is concluded that the HPSO is superior to other algorithm due to tracking performance, computational overhead decreases, reduction of search space, reduced oscillation, reduction of search space, and tracking efficiency.